Piping and instrumentation planning and maintenance system

ABSTRACT

A piping and instrumentation planning and maintenance system includes an input/output (I/O) interface for receiving a target piping and instrumentation diagram (PID) from a document source system; a processor in communication with the I/O interface; and non-transitory computer readable media in communication with the processor. The non-transitory computer readable media store instruction code, which when executed by the processor, causes the processor to classify entities and properties thereof within the target PID. The entities include one or more assets and interconnections therebetween specified in the PID. The processor compares the classified entities to a knowledge base that represents relationships between a plurality of assets and interconnections between the assets. The processor then determines, based on the comparison, whether the assets in the target PID are interconnected correctly. When the assets are not interconnected correctly, the processor generates a report to identify the assets that are not interconnected correctly. The report facilitates proactive replacement or rearrangement of assets in a facility associated with the target PID.

BACKGROUND Field

This application generally relates to mechanical systems. In particular,this application describes a piping and instrumentation planning andmaintenance system.

Description of Related Art

In the process industry, large process facilities are typicallyrepresented by a piping and instrumentation diagram (PID). The PID is adetailed diagram which shows the piping and vessels utilized in aprocess flow of a facility, together with the instrumentation andcontrol devices the facility. Creation of a PID may be performed by oneor more process experts with significant industry experience.

The PID for a given facility may evolve over time due to changes in thefacility. Some changes to the facility (e.g., change in the model of apump) may not be compatible with existing infrastructure of thefacility. For example, the pump may not be suitable for pumping aparticular fluid. Such incompatibilities may be difficult to determineas the facility evolves because people with industry knowledge of thefacility may leave and/or the complexity of the facility may be toogreat. The incompatibility may result in a catastrophic failure ofequipment at the facility.

SUMMARY

In a first aspect, a piping and instrumentation planning and maintenancesystem includes an input/output (I/O) interface for receiving a targetpiping and instrumentation diagram (PID) from a document source system;a processor in communication with the I/O interface; and non-transitorycomputer readable media in communication with the processor. Thenon-transitory computer readable media stores instruction code, whichwhen executed by the processor, causes the processor to classifyentities and properties thereof within the target PID. The entitiesinclude one or more assets and interconnections therebetween specifiedin the PID. The processor compares the classified entities to aknowledge base that represents relationships between a plurality ofassets and interconnections between the assets. The processor thendetermines, based on the comparison, whether the assets in the targetPID are interconnected correctly. When the assets are not interconnectedcorrectly, the processor generates a report to identify the assets thatare not interconnected correctly. The report facilitates proactivereplacement or rearrangement of assets in a facility associated with thetarget PID.

In a second aspect, a method for planning and maintaining piping andinstrumentation includes receiving a target piping and instrumentationdiagram (PID) from a document source system and classifying entities andproperties thereof within the target PID. The entities include one ormore assets and interconnections therebetween specified in the PID. Themethod further includes comparing the classified entities to a knowledgebase that represents relationships between a plurality of assets andinterconnections between the plurality of assets. The method furtherincludes determining, based on the comparison, whether the assets in thetarget PID are interconnected correctly. When the assets are notinterconnected correctly, the method includes generating a report toidentify the assets that are not interconnected correctly. The reportfacilitates proactive replacement or rearrangement of assets in afacility associated with the target PID.

In a third aspect, a non-transitory computer readable media that storesinstruction code for planning and maintaining piping and instrumentationis provided. The instruction code is executable by a machine for causingthe machine to receive a target piping and instrumentation diagram (PID)from a document source system; and classify entities and propertiesthereof within the target PID. The entities include assets andinterconnections therebetween specified in the PID. The machine comparesthe classified entities to a knowledge base that representsrelationships between a plurality of assets and interconnections betweenthe plurality of assets; and determines, based on the comparison,whether the assets in the target PID are interconnected correctly. Whenthe one or more assets are not interconnected correctly, the machinegenerates a report to identify the assets that are not interconnectedcorrectly. The report facilitates proactive replacement or rearrangementof assets in a facility associated with the target PID.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary environment that includes varioussystems/devices that facilitate planning and maintenance of piping andinstrumentation;

FIG. 2 illustrates exemplary processes implemented by a piping andinstrumentation management system (PIPMS) of the environment;

FIGS. 3-5, and 7 illustrate various screenshots of a user interfacegenerated by the PIPMS;

FIG. 6 illustrates an exemplary knowledge base generated by the PIPMS;

FIG. 8 illustrates operations performed by the PIPMS in answering aquery related to assets identified in the knowledge base; and

FIG. 9 illustrates an exemplary computer system that may form part of orimplement the systems described in the figures or in the followingparagraphs.

DETAILED DESCRIPTION

The embodiments described below overcome the problems described above byproviding a piping and instrumentation planning and maintenance systemthat maintains a knowledge base that describes the relationships betweenassets (e.g., pumps, valve, pipes, etc.) of a facility. The knowledgebased is accessibly to a facility operator and allows the facilityoperator to quickly identify equipment that is compatible with existingequipment of the facility.

The knowledge base is generated by processing existing PIDs through apipeline of models that are trained to detect various features of thePIDs and to update the knowledge base accordingly.

In some implementations, a new/proposed PID may be processed through thesystem and the system may flag incompatibilities in assets of theproposed PID based on information found in the knowledge graph.

In other implementations, the system may determine that an existingfacility is operating equipment in an incorrect manner based oninformation in a PID that describes the facility. The system maycommunicate information to the facility to adjust parameters of theequipment to bring the equipment into safe operating conditions.

FIG. 1 illustrates an exemplary environment 100 in which a piping andinstrumentation planning and maintenance system (PIPMS) exists.Exemplary systems/devices of the environment 100 include the PIPMS 102,a document source system 104, a PID expert terminal 105, and a facilityoperator terminal 106 that is in communication with facility equipment108.

The various entities of the environment 100 may communicate with oneanother via a network 107, such as the Internet and may correspond tocomputer systems such as an Intel®, AMD®, or PowerPC® based computersystem or a different computer system and can include applicationspecific computer systems. The computer systems may include an operatingsystem, such as Microsoft Windows®, Linux, Unix® or other operatingsystem. The terminals may be desktop PCs and/or mobile terminals.

The document source system 104 may correspond to one or more computersthat store PIDs, engineering schematics, bill of materials,specification documents, safety documents, etc. The document sourcesystem 104 may implement one or more APIs that facilitate communicatinginformation to the PIPMS 102.

The PID expert terminal 105 may correspond to a computer at which a PIDexpert reviews PIDs that have been classified by the PIPMS 102. In thisregard, the PID expert terminal 105 may be configured to facilitatecommunicating information with the PIPMS 102 via one or more APIs of thePIPMS 102.

The facility operator terminal 106 may correspond to a computer at whicha facility operator can review the way in which PIDs have beenclassified. The facility operator terminal 106 may receive queries fromthe operator as to how various types of equipment/assets may beconnected or used. In some implementations, the facility operatorterminal 106 is in communication with facility equipment 108 thatfacilitates monitoring and controlling the facility equipment 108.

The document source system 104, PID expert terminal 105, and facilityoperator terminal 106 may be configured to communicate with the PIPMS102 via an API such as a webserver API, a SOAP-based web service, aRESTful API, and/or a different type of API.

The PIPMS 102 may include a processor 125, input/output subsystem 110,and an AI subsystem 115. The PIPMS 102 may include other subsystems.

The I/O subsystem 110 of the PIPMS 102 facilitates communications withentities outside of the PIPMS 102. In this regard, the I/O subsystem 110may be configured to dynamically determine the communication methodologyutilized by entities of the environment 100 for communicatinginformation to the entities using the determined communicationmethodology. For example, the I/O subsystem 110 may determine that afirst entity utilizes a RESTful API and may, therefore, communicate withthe entity using a RESTful communication methodology.

As described in more detail below, the I/O subsystem 110 may implement aweb browser to facilitate generating one or more web-based interfaces orscreenshots through which users of document source system 104, PIDexpert terminal 105, facility operator terminal 106, and/or othersystems may interact with the PIPMS 102. The web browser may implement aweb services interface to facilitate automating some of the web-basedfunctionality via a computer. For example, one or more of the entitiesof the environment 100 may utilize the web services interfaces to accessinformation stored by the PIPMS 102 and/or to communicate information tothe PIPMS 102.

The AI subsystem 115 may correspond to hardware specifically configuredto perform or assist in the performance of object detection andclassification of entities of a PID. For example, the AI subsystem 115may utilize OpenCV, a sliding window approach, predictive machinelearning models, and/or other machine learning techniques to detect andclassify entity of a PID.

The AI subsystem 115 may be further configured to ascertain the meaningof various documents, such as manuals, safety datasheets, etc. that mayoriginate from the document source system 104. For example, the AIsubsystem 114 may implement various natural language processingtechniques such as latent Dirichlet allocation (LDA) to identify topicsassociated with documents, hierarchical density based cluster analysis(H-DBSCAN) to group parts of documents under one or more topics,Knuth-Morris-Pratt string searching to locate and extract occurrences ofa certain words within documents, possibly linear clustering algorithmsto mine text data, and/or other techniques.

The CPU 125 executes instruction code stored in a memory device 127 forcoordinating activities performed between the various subsystems. Theprocessor 125 may correspond to a stand-alone computer system such as anIntel®, AMD®, or PowerPC® based computer system or a different computersystem and can include application specific computer systems. Thecomputer systems may include an operating system, such as MicrosoftWindows®, Linux, Unix® or other operating system.

It is contemplated that the I/O subsystem 110, AI subsystem 115, and anyother subsystem referenced herein may correspond to a stand-alonecomputer system such as an Intel®, AMD®, or PowerPC® based computersystem or a different computer system and can include applicationspecific computer systems. The computer systems may include an operatingsystem, such as Microsoft Windows®, Linux, Unix® or other operatingsystem. It is also contemplated that operations performed on the varioussubsystems may be combined into a fewer or greater number of subsystemsto facilitate speed scaling, cost reductions, etc.

FIG. 2 illustrates exemplary processes implemented by the PIPMS 102 thatfacilitate planning and maintenance of various piping andinstrumentation systems. The processes generally fall under modelmanagement processes 205 and digitization processes 210. The variousprocesses and subprocesses/operations thereof may be implemented viainstruction code executed by the processor 125 of the PIPMS 102 forcausing the processor to perform the processes.

The model management processes 205 generate models for identifyingfeatures of PIDs and for maintaining those models. The models generatedand maintained by the model management processes 205 are stored in amodel catalog. In this regard, a representative number of exemplarytraining PIDs (e.g., 40 PIDs) may be obtained from, for example, thedocument source system 104 and processed through the models. Variousfeatures of the training PIDs may have been previously determined by anexpert. For example, different features such as assets, connections, andtext may have been determined by the expert.

Each PID may be processed through one or more models to train the modelsto identify features of the training PIDs. For example, a first modelmay be trained to identify asset features such as pumps, valves,regulators, etc. A second model may be trained to identify connectionfeatures between the assets such as pipes, channels, etc. A third modelmay be trained to identify text features surrounding assets andconnections in the training PIDs such as pressure and temperatureattributes, model number and manufacturer attributes, etc.

The output of each model may be reviewed by an expert with knowledge ofthe information in the training PIDs via, for example, the PID expertterminal 105. In this regard, the output of the model may correspond tofeatures identified within a given training PID (e.g., assets,connections, text). The expert may review the features identified by themodels and approve, reject, and/or change the features associated withthe PID.

If the expert has modified the features identified for a given trainingPID, that PID may be reprocessed through the models to train the modelsfurther.

The digitization processes 210 correspond to a pipeline of processesthat generates a knowledge base that describes assets in one or moretarget PIDs, the connections/relationships between those assets, and anyfeatures of the assets, based on one or more models stored in the modelcatalog.

At the beginning of the pipeline, a target PID is received andpreprocessed. For example, the target PID may have been in paper formand may have been scanned to produce a digital image of the PID.Preprocessing may involve determining whether the scanned image qualityof the target PID is suitable for subsequent processing steps. Forexample, preprocessing may involve determining whether the resolution ofthe PID image is sufficiently high, whether noise in the PID image isbelow a threshold, whether the contrast of the PID image is sufficientlyhigh. In some cases, when the PID image does not meet the criterianecessary for subsequent processing steps, an operator may be notifiedthat the PID image has to be rescanned. In other cases, the PIPMS 102may attempt to automatically remedy the issues. For example, theresolution and/or contrast level of the PID image may be adjusted tofacilitate subsequent processing steps. Image filtering techniques suchas noise filtering, etc. may be applied to the image to remove artifactsfrom the PID image that would otherwise impede subsequent processingsteps.

After the PID image has been appropriately processed, a specific PIDprocessing pipeline may be selected and applied to the PID image. Inthis regard, specific PID processing pipelines may have been previouslyspecified by an expert with knowledge of the information contained in agiven type of PID via, for example, the PID expert terminal 105.

FIGS. 3 and 4 illustrate various user interfaces generated by the PIPMS102 that facilitate creating specific PID processing pipelines fordifferent types of PIDs.

Referring to FIG. 3, a user interface 300 may be presented to the expertthat facilitates selection of models 305 that are available in the assetcatalog. Exemplary models 305 that may be selected include, for example,models for detecting assets, asset connections, pipes, and text in PIDs.

Referring to FIG. 4, the operator may connect the models together toform a process pipeline 405 capable of classifying a PID. For example,the expert may drag and drop a “create slices” process 410 a, “detectassets” model 410 b, “asset enhancement” model 410 d, and a “combinedslices” process 410 c into the workspace of the user interface. The“create slices” process 410 a may be configured to segment a PID imageof a given size into a number of smaller sized images. Segmentation maybe required when the PID image is too large to process by subsequentmodels or subprocesses.

The “detect assets” model 410 b is configured to identify differentportions of the PID image that correspond to assets (i.e., valves,pumps, pipes, etc.). For example, as illustrated in FIG. 5, some of theregions that may be detected by the “detect assets” model 410 b ascorresponding to assets may include regions a-h.

The “asset enhancement” model 410 d is configured to identify text andthe meaning of the text in the PID image that is near assets detected bythe “detect assets” model 410 b. For example, the “asset enhancement”model 410 d may detect text near an image of a pump and determine thatthe text corresponds to, for example, a pump model number, fluidpressures, fluid temperatures, flow rates, etc.

The “combined slices” process 410 c is utilized in combination with the“create slices” process 410 a to recombine the output produced by agiven model. For example, in the illustrated case, the “detect assets”model 410 b processed different segments of the PID image because thePID image itself was too large to process. The output of the “detectassets” model 410 b is, therefore, based on individual segments. The“combine slices” process 410 c combines the output from the model thatis associated with the different segments. In some cases, the segmentsof the PID may overlap to some extent. That is, the same asset mayappear in adjacent image segments and, therefore, appear as duplicatesin the output of the “detect assets” model 410 b. The “combined slices”process 410 c may remove any duplicates when combing the “detect assets”model 410 b output results for the different image segments.

In some cases, different process flows may be generated for differenttypes of PIDs. In this regard, the type of PID may be checked initially,and the appropriate process flow for classifying information in the PIDmay be selected and utilized to process the PID. In some implementation,determination of the type of PID document may be based on, for example,metadata within the PID. In other implementations, an expert maydetermine the PID type and select the appropriate process flow forclassifying the PID.

Referring back to FIG. 2, after selection of the specific PID processingpipeline, the PIPMS 102 may execute the PID processing pipeline againstthe target PID. After processing the target PID, the PIPMS 102 maygenerate the user interface 500 of FIG. 5.

Referring to FIG. 5, the user interface 500 may include an image of thetarget PID 505 or section of the target PID. The user interface 500 mayalso include a selection dialog 510 that facilitates selection of one ormore assets displayed in the image 505. Upon selection of an assets inthe selection dialog 510, the user interface 500 may be updated tohighlight assets corresponding to the selection, as illustrated by thehighlighted regions a-h.

In some implementations, the user interface 500 is configured to allowan expert to specify whether the target PID was correctly classified. Inthis regard, the user interface 500 may be configured to allow theexpert to modify the classification given to an asset. For example, aparticular asset may have been classified as a valve when in fact itcorresponds to a pump. Where reclassification of a target PID isrequired, the reclassified PID may be sent back to the model managementprocesses 205 to retrain one or more of the models.

If the classification of the PID is determined to be correct, theknowledge base may be updated to describe assets in the target PID, theconnections/relationship between those assets, and any features of theassets.

An exemplary knowledge base that may be generated is illustrated in FIG.6. Referring to the key in FIG. 6, the exemplary knowledge basecorresponds to an ontology that includes document, asset, connector, andattribute nodes.

The document nodes include information that identifies a particular PIDwhere information was extracted from. All assets found in that PID maybe connected to this node.

Asset nodes correspond to assets extracted from the PID (e.g., pumps,valves, pipes, etc.). The asset nodes may be linked to documents relatedto the asset (e.g., manuals, technical bulletins, etc.) as well as otherassets via connector nodes. In this regard, the documents may have beenprocessed via the AI subsystem 115 to identify documents relevant to thevarious assets. The knowledge base may have been upgraded by the AIsubsystem 115 accordingly.

The connector nodes are similar to edges but contain additionalinformation. For example, in this case, the connector nodes correspondto pipes that have liquid flowing through them. The connector nodes areconnected to different assets such as a pump to a tank through a valve.

Attribute nodes augment information about the PID, assets, andconnectors. Attribute nodes specify information that Includes metadatainformation such as piping material, a type of liquid flowing through anasset, manufacturer information of an asset, service history of theasset, etc. In some cases, attribute nodes may be added to the knowledgebase by processes other than the digitization processes.

In some implementations, a large number (e.g., hundreds) of target PIDsthat have already been scanned into a digital form may be processedthrough the digitization processes 210. The target PIDs may be processedsequentially or in parallel.

Metadata in each target PID may be analyzed to determine the type ofPID. The detected type may be used to select an appropriate PIDprocessing pipeline to apply to a given PID. The knowledge base 212 maybe automatically updated with information that describes a given targetPID. That is the knowledge base 212 may be updated without firstrequiring expert review of the way in which a given target PID wasclassified.

FIG. 7 illustrates an exemplary user interface 700 that may be generatedby the PIPMS 102 to show the status of the digitization processes 210.Referring to FIG. 7, the user interface 700 may display the number ofdocuments that have been processed (e.g., 28), the number of PIDscurrently being processed (e.g., 17), and the number of rejected PIDs(e.g., 9). Rejected PIDs may be routed to an appropriate to an expertfor further analysis.

As noted above, the knowledge base 212 is updated with information thatdescribes assets and the interconnections therebetween in target PIDsthat are processed through the digitization processes 210. Therefore,with each new target PID, the knowledge base 212 grows or is enhanced.

After a sufficiently large number of target PID's have been processed,the information in the knowledge base 212 may become sufficiently robustto facilitate providing, by the PIPMS 102, recommendations on howdifferent types of assets should be connected. For example, a userinterface may be generated that allows a facility operator to specify,via the facility operator terminal 106, the types of assets that need tobe connected, along with parameters to be associated with the assets.For example, referring to FIG. 8, at operation 800, the PIPMS 102 mayreceive a query from the facility operator, via the user interface, suchas “What type of pipe can be connected to the X2110 valve.”

At operation 805, the PIPMS 102 may search the knowledge base 212 forassets that are valves named “X2110.” The PIPMS 102 may then determineany assets that are pipes connected to the valve assert.

At operation 810, one or more suggested answers to the query may becommunicated to the facility operator. Following the example above, thePIPMS 102 may provide a list of pipes that may be used with the X2110valve.

The query may be more complicated. For example, the query may be “Whattype of pipe can be connected to the X2110 valve and that operates at300° C. at 500 PSIa.” In this case, the PIPMS 102 may first locateassets corresponding to the X2110 valve. The PIPMS 102 may then searchfor connected pipes specified to operate at 300° C. or higher and at 500PSIa or higher.

In other implementations, the knowledge base 212 may be utilized topredict whether the arrangement of assets in a given target PID iscorrect. This information may be useful to, for example, a facilityoperator and may allow the operator to detect problems before theyoccur. For example, upon receiving an indication of a problem with a PIDthat describes an existing facility, the operator may halt production atthe facility and change the problematic arrangement of equipment.

In some implementations, after detecting a problem with an existingfacility, the PIPMS 102 may communicate instructions to potentiallyproblematic equipment at the facility to operate in a way as to avoidthe potential problem. For example, instructions to shut down theequipment, to lower the output pressure or flow of the equipment, etc.may be communicated to the equipment of the facility (108, FIG. 1) viathe facility operator terminal 106. The equipment may in turn respond tothe instruction thereby avoiding a potentially catastrophic failure ofthe facility.

In yet other implementations, information in the knowledge base mayindicate a useful life of certain assets. In this case, the PIPMS 102may determine that a given asset specified in a given PID is nearend-of-life. The PIPMS 102 may automatically generate an order to anequipment manufacture for a replacement asset.

FIG. 9 illustrates a computer system 900 that may form part of orimplement the systems, environments, devices, etc., described above. Thecomputer system 900 may include a set of instructions 945 that theprocessor 905 may execute to cause the computer system 900 to performany of the operations described above. The computer system 900 mayoperate as a stand-alone device or may be connected, e.g., using anetwork, to other computer systems or peripheral devices.

In a networked deployment, the computer system 900 may operate in thecapacity of a server or as a client computer in a server-client networkenvironment, or as a peer computer system in a peer-to-peer (ordistributed) environment. The computer system 900 may also beimplemented as or incorporated into various devices, such as a personalcomputer or a mobile device, capable of executing instructions 945(sequential or otherwise) causing a device to perform one or moreactions. Further, each of the systems described may include a collectionof subsystems that individually or jointly execute a set, or multiplesets, of instructions to perform one or more computer operations.

The computer system 900 may include one or more memory devices 910communicatively coupled to a bus 920 for communicating information. Inaddition, code operable to cause the computer system to performoperations described above may be stored in the memory 910. The memory910 may be a random-access memory, read-only memory, programmablememory, hard disk drive or any other type of memory or storage device.

The computer system 900 may include a display 930, such as a liquidcrystal display (LCD), a cathode ray tube (CRT), or any other displaysuitable for conveying information. The display 930 may act as aninterface for the user to see processing results produced by processor905.

Additionally, the computer system 900 may include an input device 925,such as a keyboard or mouse or touchscreen, configured to allow a userto interact with components of system 900.

The computer system 900 may also include a disk or optical drive unit915. The drive unit 915 may include a computer-readable medium 940 inwhich the instructions 945 may be stored. The instructions 945 mayreside completely, or at least partially, within the memory 910 and/orwithin the processor 905 during execution by the computer system 900.The memory 910 and the processor 905 also may include computer-readablemedia as discussed above.

The computer system 900 may include a communication interface 935 tosupport communications via a network 950. The network 950 may includewired networks, wireless networks, or combinations thereof. Thecommunication interface 935 may enable communications via any number ofcommunication standards, such as 802.11, 802.12, 802.20, WiMAX, cellulartelephone standards, or other communication standards.

Accordingly, methods and systems described herein may be realized inhardware, software, or a combination of hardware and software. Themethods and systems may be realized in a centralized fashion in at leastone computer system or in a distributed fashion where different elementsare spread across interconnected computer systems. Any kind of computersystem or other apparatus adapted for carrying out the methods describedherein may be employed.

The methods and systems described herein may also be embedded in acomputer program product, which includes all the features enabling theimplementation of the operations described herein and which, when loadedin a computer system, is able to carry out these operations. Computerprogram as used herein refers to an expression, in a machine-executablelanguage, code or notation, of a set of machine-executable instructionsintended to cause a device to perform a particular function, eitherdirectly or after one or more of a) conversion of a first language,code, or notation to another language, code, or notation; and b)reproduction of a first language, code, or notation.

While methods and systems have been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the claims. Therefore, it is intended thatthe present methods and systems not be limited to the particularembodiment disclosed, but that the disclosed methods and systems includeall embodiments falling within the scope of the appended claims.

The invention claimed is:
 1. A piping and instrumentation planning and maintenance system comprising: an input/output (I/O) interface for receiving a target piping and instrumentation diagram (PID) from a document source system; a processor in communication with the I/O interface; and non-transitory computer readable media in communication with the processor that stores instruction code, which when executed by the processor, causes the processor to: classify entities and properties thereof within the target PID, wherein the entities include one or more assets and interconnections therebetween specified in the PID; compare the classified entities to a knowledge base that represents relationships between a plurality of assets and interconnections between the plurality of assets; determine, based on the comparison, whether the one or more assets in the target PID are interconnected correctly; and when the one or more assets are not interconnected correctly, generate a report to identify the one or more assets that are not interconnected correctly, wherein the report facilitates proactive replacement or rearrangement of assets in a facility associated with the target PID.
 2. The system according to claim 1, wherein the instruction code causes the processor to: process a plurality of PIDs through one or more models for identifying entities of each PID to thereby train the one or more models to identify the entities of each PID; and generate the knowledge base based on the entities identified in the plurality of PIDs.
 3. The system according to claim 2, wherein the models include an asset detection model, an asset connection model, a pipe detection model, and a text tag detection model.
 4. The system according to claim 2, wherein the instruction code causes the processor to: generate a user interface that depicts a classified representation of the target PID; receive alternative classifications for one or more of the entities of the classified representation to thereby update the classified representation of the target PID; and train the one or more models with the updated classified representation of the target PID.
 5. The system according to claim 2, wherein the PIDs correspond to image documents and at least one model is limited to processing images smaller than the PIDs, wherein the instruction code causes the processor to: split each PID into smaller images capable of being processed by the at least one model; process the smaller images through the at least one model; and combine the results of the at least one model.
 6. The system according to claim 1, wherein the knowledge base corresponds to an ontology and the ontology includes nodes that define documentation associated with assets.
 7. The system according to claim 1, wherein the instruction code causes the processor to: receive, from a terminal, a query related to the selection and arrangement of assets; generate a response to the query based on information in the knowledge base; and communicate, to the terminal, the response to the query.
 8. A method for planning and maintaining piping and instrumentation comprising: receiving a target piping and instrumentation diagram (PID) from a document source system; classifying entities and properties thereof within the target PID, wherein the entities include one or more assets and interconnections therebetween specified in the PID; comparing the classified entities to a knowledge base that represents relationships between a plurality of assets and interconnections between the plurality of assets; determining, based on the comparison, whether the one or more assets in the target PID are interconnected correctly; and when the one or more assets are not interconnected correctly, generating a report to identify the one or more assets that are not interconnected correctly, wherein the report facilitates proactive replacement or rearrangement of assets in a facility associated with the target PID.
 9. The method according to claim 8, further comprising: processing a plurality of PIDs through one or more models for identifying entities of each PID to thereby train the one or more models to identify the entities of each PID; and generating the knowledge base based on the entities identified in the plurality of PIDs.
 10. The method according to claim 9, wherein the models include an asset detection model, an asset connection model, a pipe detection model, and a text tag detection model.
 11. The method according to claim 9, further comprising: generating a user interface that depicts a classified representation of the target PID; receiving alternative classifications for one or more of the entities of the classified representation to thereby update the classified representation of the target PID; and training the one or more models with the updated classified representation of the target PID.
 12. The method according to claim 9, wherein the PIDs correspond to image documents and at least one model is limited to processing images smaller than the PIDs, wherein the method further comprises: splitting each PID into smaller images capable of being processed by the at least one models; processing the smaller images through the at least one model; and combining the results of the at least one model.
 13. The method according to claim 8, wherein the knowledge base corresponds to an ontology and the ontology includes nodes that define documentation associated with assets.
 14. The method according to claim 8, further comprising: receiving, from a terminal, a query related to the selection and arrangement of assets; generating a response to the query based on information in the knowledge base; and communicating, to the terminal, the response to the query.
 15. A non-transitory computer readable media that stores instruction code for planning and maintaining piping and instrumentation, the instruction code being executable by a machine for causing the machine to: receive a target piping and instrumentation diagram (PID) from a document source system; classify entities and properties thereof within the target PID, wherein the entities include one or more assets and interconnections therebetween specified in the PID; compare the classified entities to a knowledge base that represents relationships between a plurality of assets and interconnections between the plurality of assets; determine, based on the comparison, whether the one or more assets in the target PID are interconnected correctly; and when the one or more assets are not interconnected correctly, generate a report to identify the one or more assets that are not interconnected correctly, wherein the report facilitates proactive replacement or rearrangement of assets in a facility associated with the target PID.
 16. The non-transitory computer readable media according to claim 15, wherein the instruction code causes the machine to: process a plurality of PIDs through one or more models for identifying entities of each PID to thereby train the one or more models to identify the entities of each PID; and generate the knowledge base based on the entities identified in the plurality of PIDs.
 17. The non-transitory computer readable media according to claim 16, wherein the models include an asset detection model, an asset connection model, a pipe detection model, and a text tag detection model.
 18. The non-transitory computer readable media according to claim 16, wherein the instruction code causes the machine to: generate a user interface that depicts a classified representation of the target PID; receive alternative classifications for one or more of the entities of the classified representation to thereby update the classified representation of the target PID; and train the one or more models with the updated classified representation of the target PID.
 19. The non-transitory computer readable media according to claim 16, wherein the PIDs correspond to image documents and at least one model is limited to processing images smaller than the PIDs, wherein the instruction code causes the machine to: split the PIDs into smaller images capable of being processed by the at least one model; process the smaller images through the at least one model; and combine the results of the at least one model.
 20. The non-transitory computer readable media according to claim 15, wherein the knowledge base corresponds to an ontology and the ontology includes nodes that define documentation associated with assets. 